Reliable state monitoring in cloud datacenters
Shicong Meng, Arun K. Iyengar, et al.
CLOUD 2012
Despite having several distributed graph processing frameworks, scalable iterative processing of large graphs is a challenging problem since the graph and intermediate data need a global view of the graph topology in distributed memory. Although some systems support out-of-core iterative computations, they use a single machine and often require fast storage. In this paper, we present a new distributed iterative graph computation framework, called GraphMap, that utilizes a disk-based NoSQL database system for scalable graph processing while ensuring competitive performance. Extensive experiments on several real-world graphs show that GraphMap is more scalable and often faster than existing distributed memory-based systems for various graph processing workloads.
Shicong Meng, Arun K. Iyengar, et al.
CLOUD 2012
Kisung Lee, Raghu K. Ganti, et al.
EAI MobiQuitous 2014
Kisung Lee, Ling Liu, et al.
IEEE-TSC
Yuzhe Tang, Ling Liu, et al.
ICDCS 2014